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Due to the rapid increase in the diversity of image data, the problem ofdomain generalization has received increased attention recently. While domaingeneralization is a challenging problem, it has achieved great developmentthanks to the fast development of AI techniques in computer vision. Most ofthese advanced algorithms are proposed with deep architectures based onconvolution neural nets (CNN). However, though CNNs have a strong ability tofind the discriminative features, they do a poor job of modeling the relationsbetween different locations in the image due to the response to CNN filters aremostly local. Since these local and global spatial relationships arecharacterized to distinguish an object under consideration, they play acritical role in improving the generalization ability against the domain gap.In order to get the object parts relationships to gain better domaingeneralization, this work proposes to use the self attention model. However,the attention models are proposed for sequence, which are not expert indiscriminate feature extraction for 2D images. Considering this, we proposed ahybrid architecture to discover the spatial relationships between these localfeatures, and derive a composite representation that encodes both thediscriminative features and their relationships to improve the domaingeneralization. Evaluation on three well-known benchmarks demonstrates thebenefits of modeling relationships between the features of an image using theproposed method and achieves state-of-the-art domain generalizationperformance. More specifically, the proposed algorithm outperforms thestate-of-the-art by 2.2% and 3.4% on PACS and Office-Home databases,respectively.

Discovering Spatial Relationships by Transformers for Domain Generalization